Jandt, U. | bioRxiv Science

bioRxiv2020

Revealing the functional traits that are linked to hidden environmental factors in community assembly

AimTo identify functional traits that best predict community assembly without knowing the driving environmental factors. MethodsWe propose a new method that is based on the correlation r(XY) between two matrices of potential community composition: matrix X is fuzzy-weighted by trait similarities of species, and matrix Y is derived by Beals smoothing using the probabilities of species co-occurrences. Since matrix X is based on one or more traits, r(XY) measures how well the traits used for fuzzy-weighting reflect the observed co-occurrence patterns. We developed an optimization algorithm that identifies those traits that maximize this correlation, together with an appropriate permutational test for significance. Using metacommunity data generated by a stochastic, individual-based, spatially explicit model, we assessed the type I error and the power of our method across different simulation scenarios, varying environmental filtering parameters, number of traits and trait correlation structures. We then applied the method to real-world community and trait data of dry calcareous grassland communities across Germany to identify, out of 49 traits, the combination of traits that maximizes r(XY). ResultsThe method correctly identified the relevant traits involved in the community assembly mechanisms specified in simulations. It had high power and accurate type I error and was robust against confounding aspects related to interactions between environmental factors, strength of limiting factors, and correlation among traits. In the grassland dataset, the method identified five traits that best explained community assembly. These traits reflected the size and the leaf economics spectrum, which are related to succession and resource supply, factors that may not be always measured in real-world situations. ConclusionsOur method successfully identified the relevant traits mediating community assembly driven by environmental factors which may be hidden for not being measured or accessible at the spatial or temporal scale of the study.

ecology↗

bioRxiv2020

Widespread decline in plant diversity across six decades

Based on plant occurrence data covering all parts of Germany, we investigated changes in the distribution of 2146 plant species between 1960 and 2017. We analyzed 29 million occurrence records over an area of [~]350.000 km2 on a 5 x 5 km grid using temporal and spatio-temporal models and accounting for sampling bias. Since the 1960s, more than 70% of investigated plant species showed significant declines in nation-wide occurrence. Archaeophytes (species introduced before 1492) most strongly declined but also native plant species experienced severe declines. In contrast, neophytes (species introduced after 1492) increased in their nation-wide occurrence but not homogeneously throughout the country. Our analysis suggests that the strongest declines in native species already happened in the 1960s-80s, a time frame in which usually few data exist. Increases in neophytic species were strongest in the 1990s and 2010s. Overall, the increase in neophytes did not compensate for the loss of other species, resulting in a decrease in mean grid-cell species-richness of -1.9% per decade. The decline in plant biodiversity is a widespread phenomenon occurring in different habitats and geographic regions. It is likely that this decline has major repercussions on ecosystem functioning and overall biodiversity, potentially with cascading effects across trophic levels. The approach used in this study is transferable to large-scale trend analyses using heterogeneous occurrence data.

ecology↗